Prediction of Essential Genes based on Machine Learning and Information Theoretic Features
Prediction of Essential Genes based on Machine Learning and Information Theoretic Features
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DOI：
10.5220/0006165700810092
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发表时间：
2017
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影响因子：
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通讯作者：
Dawit Nigatu;W. Henkel
中科院分区：
文献类型：
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作者：
Dawit Nigatu;W. Henkel
计算工具使得对必需基因(EGS)的预测变得相对简单，否则这将通过昂贵而繁琐的基因敲除实验程序来完成。我们提出了一种基于机器学习的预测器，该预测器使用完全来自DNA序列的信息论特征。我们使用了熵、互信息、条件互信息和马尔可夫链模型作为特征。我们利用支持向量机分类器对15个原核生物基因组中的EGS进行了预测。对大肠杆菌、枯草杆菌和肺支原体进行五次交叉验证后，AUC得分分别为0.85、0.81和0.89。在跨有机体预测中，使用对其余细菌进行训练的模型来预测给定细菌的EGS。AUC评分0.66~0.9，平均0.8分。该分类器在大肠杆菌、枯草杆菌和不动杆菌之间一对一预测的平均AUC为0.85。我们预报器的性能可与最新和最先进的预报器相媲美。考虑到我们在一个复杂得多的问题上只使用了序列信息，所取得的结果是非常
Computational tools have enabled a relatively simple prediction of essential genes (EGs), which would otherwise be done by costly and tedious gene knockout experimental procedures. We present a machine learning based predictor using information-theoretic features derived exclusively from DNA sequences. We used entropy, mutual information, conditional mutual information, and Markov chain models as features. We employed a support vector machine (SVM) classifier and predicted the EGs in 15 prokaryotic genomes. A fivefold cross-validation on the bacteria E. coli, B. subtilis, and M. pulmonis resulted in AUC score of 0.85, 0.81, and 0.89, respectively. In cross-organism prediction, the EGs of a given bacterium are predicted using a model trained on the rest of the bacteria. AUC scores ranging from 0.66 to 0.9 and averaging 0.8 were obtained. The average AUC of the classifier on a one-to-one prediction among E. coli, B. subtilis, and Acinetobacter is 0.85. The performance of our predictor is comparable with recent and state-of-the art predictors. Considering that we used only sequence information on a problem that is much more complicated, the achieved results are very